scholarly journals SECURING CLOUD DATA AGAINST CYBER-ATTACKS USING HYBRID AES WITH MHT ALGORITHM

2020 ◽  
pp. 561-568
Author(s):  
N. Jayapandian
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Cost Minimization ◽  
Data Communication ◽  
Cyber Attacks ◽  
Cloud Data ◽  
Aes Algorithm ◽  
Data Process ◽  
Research Article

Cloud computing is dealing with large amount of data during data communication. This data processing is named as big data. The big data is growth of the demand in accessing the storage, computation and communication. This big data has the major defects. A raising issue in emerging big data is cost minimization. The architecture of big data ranges over multiple machines and cluster which have sub system. The major challenge of this big data is pre-processing and analysing the data patterns. This research article is dealing with different data pre-processing and secure data storage. There are many research challenges during this data process. The possible gap and drawbacks in the technology are identified through this survey and the efficient big data service is provided through MHT and AES algorithm. The main aim of this proposed method is to provide better data security during larger data process. The proposed hybrid MHT with AES algorithm is to minimize the encryption and decryption time apart from that it reduces the attacker ratio. All these parameters automatically increase the Quality of Service.

scholarly journals The Construction and Thinking of Cloud Computing and Big Data Technology in Smart Campus

2019 ◽  
Vol 3 (2) ◽  
pp. 152
Author(s):  
Xianglan Wu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Information Technologies ◽  
Rapid Development ◽  
Smart Campus ◽  
Huge Data ◽  
New Information ◽  
Big Data Technology

<p>In today's society, the rise of the Internet and rapid development make every day produce a huge amount of data. Therefore, the traditional data processing mode and data storage can not be fully analyzed and mined these data. More and more new information technologies (such as cloud computing, virtualization and big data, etc.) have emerged and been applied, the network has turned from informationization to intelligence, and campus construction has ushered in the stage of smart campus construction.The construction of intelligent campus refers to big data and cloud computing technology, which improves the informatization service quality of colleges and universities by integrating, storing and mining huge data.</p>

scholarly journals Implementation of Round Key AES with Honey Technique for Securing the Public Cloud Data Storage

2018 ◽  
Vol 7 (3.1) ◽  
pp. 128
Author(s):  
Selvam L ◽  
Arokia Renjit J
Keyword(s):  
Data Storage ◽  
Cyber Security ◽  
Public Cloud ◽  
Secret Key ◽  
Cloud Data ◽  
Plain Text ◽  
The Public ◽  
Aes Algorithm ◽  
Cloud Data Storage ◽  
Brute Force Attack

Recent security incidents on public cloud data storage had raised concerns on cloud data security. Development in the hacking area has risen in the past few years. Due to this, Cyber Security is needed which plays an important role to cover the secret information. Currently, the attack of challenging channel is both the symmetric as well as the asymmetric encryption algorithm. Since, in both criteria the secret key has to be transmitting through a security challenging channel. For this many techniques have been put forward. The Main focus is on the vulnerabilities of the private keys while hoarded in different places for the fast utilization of the round key of the AES algorithm. In the view of the hackers, extracting the private key is nearly as same as obtaining the plain text itself. So, the honey encryption technique is used to futile the attacker by producing the fake key for each and every try of the Key puncher. An indication will be generated automatically to the storage manager when an attempt is made by the attacker. The Honey encryption is the best algorithm to overcome the drawbacks of the AES algorithm but it has some time constraints which are also eliminated here. Thus, eliminating the Brute Force Attack and providing a secure system for storing the secret key.  

scholarly journals Towards Big Data Analytics in the e-Learning Space

2019 ◽  
Vol 19 (3) ◽  
pp. 16-24 ◽  
Author(s):  
Ivan P. Popchev ◽  
Daniela A. Orozova
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Students At Risk ◽  
Learning Space ◽  
E Learning ◽  
New Research ◽  
Big Data Storage

Abstract The issues related to the analysis and management of Big Data, aspects of the security, stability and quality of the data, represent a new research, and engineering challenge. In the present paper, techniques for Big Data storage, search, analysis and management in the area of the virtual e-Learning space and the problems in front of them are considered. A numerical example for explorative analysis of data about the students from Burgas Free University is applied, using instrument for Data Mining of Orange. The analysis is a base for a system for localization of students at risk.

scholarly journals Security in Cloud Computing Using Hash Algorithm: A Neural Cloud Data Security Model

2018 ◽  
Vol 12 (6) ◽  
pp. 143 ◽  
Author(s):  
Osama Harfoushi ◽  
Ruba Obiedat
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Security Model ◽  
Application Development ◽  
Cloud Data ◽  
Computing Services ◽  
Cloud Computing System ◽  
Cloud Computing Services

Cloud computing is the delivery of computing resources over the Internet. Examples include, among others, servers, storage, big data, databases, networking, software, and analytics. Institutes that provide cloud computing services are called providers. Cloud computing services were primarily developed to help IT professionals through application development, big data storage and recovery, website hosting, on-demand software delivery, and analysis of significant data patterns that could compromise a system’s security. Given the widespread availability of cloud computing, many companies have begun to implement the system because it is cost-efficient, reliable, scalable, and can be accessed from anywhere at any time. The most demanding feature of a cloud computing system is its security platform, which uses cryptographic algorithm levels to enhance protection of unauthorized access, modification, and denial of services. For the most part, cloud security uses algorithms to ensure the preservation of big data stored on remote servers. This study proposes a methodology to reduce concerns about data privacy by using cloud computing cryptography algorithms to improve the security of various platforms and to ensure customer satisfaction.

scholarly journals Relevance of Big Data Technologies to and users in Context of Social Networking Applications

2020 ◽  
Vol 8 (6) ◽  
pp. 4182-4186
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Data Storage ◽  
Vital Role ◽  
Research Article ◽  
The Social ◽  
Big Data Technologies ◽  
Social Media Platforms ◽  
Big Data Storage

Unremitting generation of data by various data analytics platforms, ubiquitous ,edge nodes and social networks in the concurrent scenario has shaped the exceptional amount of data in volume, velocity, veracity, variety and value. Exceptional data have made traditional information technology and method unfeasible to cope up amid. This exceptional data has been termed as Big Data. Social media is one of the most important sources of Big Data. social media is a constituent of Big Data. Besides Big Data plays a vital role in moving forward the Social Networking Applications to innovate and enhance the experience of users. Various technologies are factored for Big Data storage, processing and analysis in the context of social networking. This paper investigates these technologies which are being used by social networking applications with their relevance to the end users. The research article provides a relevance computation of various social media platforms. It further summarizes a visualization of the use of the platforms in their contribution to the big data.

scholarly journals A Comprehensive Analysis of M-ary PSK and M-ary QAM Schemes

2021 ◽  
pp. 63-71
Author(s):  
Anas Tukur Balarabe ◽  
Ahmad Rufa’i ◽  
Zahriya Lawal Hassan ◽  
Muhammad Bello Aliyu
Keyword(s):  
Communication Technology ◽  
Additive White Gaussian Noise ◽  
Data Communication ◽  
Communication Technologies ◽  
Multipath Fading ◽  
Research Article ◽  
Digital Modulations ◽  
Modulation Schemes ◽  
Digital Circuitry

In the last few decades, data communication has recorded massive improvements. These improvements were brought about by advancement in digital circuitry, its availability and persistent reduction in cost. Before the advancement of digital communication technology, analogue communication was the dominant means of transmitting data. As the internet expands across the globe, the need to transfer data over long distances increases. However, the major problem with analogue communication is that the quality of signals is lost with distance. Also, it has minimal security and does not support data integration. Digital communications provided an alternative to analogue communication. Today, digital modulations have become part and parcel of the present and future communication technologies. Despite the advantages of these schemes, the traditional channel impairments, such as noise, can affect their performance. Moreover, data transmission is mostly done over wireless channels, which are very unpredictable and are characterised by multipath fading effects. This paper presents a short research article that presents a study of digital modulation schemes (M-ary QAM and M-ary PSK) using MATLAB/Simulink under Additive White Gaussian Noise (AWGN). The result shows that, among the three modulation schemes compared, QAM has the best BER performance with minimal energy consumption.

Big Data Security Framework for Distributed Cloud Data Centers

2017 ◽  
pp. 288-310 ◽  
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Vijayakumar V.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Efficiency ◽  
Rapid Development ◽  
Data Generation ◽  
Privacy And Security ◽  
Cloud Data ◽  
Efficiency And Effectiveness ◽  
Cloud Data Storage

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

Big Data Security Framework for Distributed Cloud Data Centers

Web Services ◽  
2019 ◽  
pp. 240-257
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Vijayakumar V.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Efficiency ◽  
Rapid Development ◽  
Data Generation ◽  
Privacy And Security ◽  
Cloud Data ◽  
Efficiency And Effectiveness ◽  
Cloud Data Storage

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

Big Data Security Framework for Distributed Cloud Data Centers

2018 ◽  
pp. 589-607 ◽  
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Vijayakumar V.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Efficiency ◽  
Rapid Development ◽  
Data Generation ◽  
Privacy And Security ◽  
Cloud Data ◽  
Efficiency And Effectiveness ◽  
Cloud Data Storage

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

BIG DATA PROCESSING: BIG CHALLENGES AND OPPORTUNITIES

2012 ◽  
Vol 13 (03n04) ◽  
pp. 1250009 ◽  
Author(s):  
CHANGQING JI ◽  
YU LI ◽  
WENMING QIU ◽  
YINGWEI JIN ◽  
YUJIE XU ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Management ◽  
Data Storage ◽  
Large Scale ◽  
Distributed Applications ◽  
Big Data Processing ◽  
Cloud Data ◽  
Management Platform ◽  
Challenges And Opportunities

With the rapid growth of emerging applications like social network, semantic web, sensor networks and LBS (Location Based Service) applications, a variety of data to be processed continues to witness a quick increase. Effective management and processing of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing techniques from system and application aspects. First, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including definition of big data, big data management platform, big data service models, distributed file system, data storage, data virtualization platform and distributed applications. Following the MapReduce parallel processing framework, we introduce some MapReduce optimization strategies reported in the literature. Finally, we discuss the open issues and challenges, and deeply explore the research directions in the future on big data processing in cloud computing environments.

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